scholarly journals The Job-search Problem with Incomplete Information

2015 ◽  
Vol 55 ◽  
pp. 159-164
Author(s):  
Vladimir Mazalov ◽  
Elena Konovalchikova
Keyword(s):  
Incomplete Information ◽  
Job Search ◽  
Search Problem

The job search problem as an employer–candidate game

1990 ◽  
Vol 27 (04) ◽  
pp. 815-827 ◽  
Author(s):  
J. M. Mcnamara ◽  
E. J. Collins
Keyword(s):  
Nash Equilibrium ◽  
Decision Maker ◽  
Job Search ◽  
Infinite Sequence ◽  
Search Problem ◽  
Infinite Population ◽  
Countable Sequence ◽  
Unique Pair ◽  
Two Populations

In the standard job search problem a single decision-maker (say an employer) has to choose from a sequence of candidates of varying fitness. We extend this formulation to allow both employers and candidates to make choices. We consider an infinite population of employers and an infinite population of candidates. Each employer interviews a (possibly infinite) sequence of candidates for a post and has the choice of whether or not to offer a candidate the post. Each candidate is interviewed by a (possibly infinite) sequence of employers and can accept or reject each offer. Each employer seeks to maximise the fitness of the candidate appointed and each candidate seeks to maximise the fitness of their eventual employer. We allow both discounting and/or a cost per interview. We find that there is a unique pair of policies (for employers and candidates respectively) which is in Nash equilibrium. Under these policies each population is partitioned into a finite or countable sequence of subpopulations, such that an employer (candidate) in a given subpopulation ends up matched with the first candidate (employer) encountered from the corresponding subpopulation. In some cases the number of non-empty subpopulations in the two populations will differ and some members of one population will never be matched.

The job search problem as an employer–candidate game

1990 ◽  
Vol 27 (4) ◽  
pp. 815-827 ◽  
Author(s):  
J. M. Mcnamara ◽  
E. J. Collins
Keyword(s):  
Nash Equilibrium ◽  
Decision Maker ◽  
Job Search ◽  
Infinite Sequence ◽  
Search Problem ◽  
Infinite Population ◽  
Countable Sequence ◽  
Unique Pair ◽  
Two Populations

In the standard job search problem a single decision-maker (say an employer) has to choose from a sequence of candidates of varying fitness. We extend this formulation to allow both employers and candidates to make choices. We consider an infinite population of employers and an infinite population of candidates. Each employer interviews a (possibly infinite) sequence of candidates for a post and has the choice of whether or not to offer a candidate the post. Each candidate is interviewed by a (possibly infinite) sequence of employers and can accept or reject each offer. Each employer seeks to maximise the fitness of the candidate appointed and each candidate seeks to maximise the fitness of their eventual employer. We allow both discounting and/or a cost per interview. We find that there is a unique pair of policies (for employers and candidates respectively) which is in Nash equilibrium. Under these policies each population is partitioned into a finite or countable sequence of subpopulations, such that an employer (candidate) in a given subpopulation ends up matched with the first candidate (employer) encountered from the corresponding subpopulation. In some cases the number of non-empty subpopulations in the two populations will differ and some members of one population will never be matched.

A novel job search problem in hybrid uncertain environment

2013 ◽  
Vol 12 (3) ◽  
pp. 249-261
Author(s):  
Guoli Wang ◽  
Wansheng Tang ◽  
Ruiqing Zhao
Keyword(s):  
Job Search ◽  
Search Problem ◽  
Uncertain Environment

The job-search problem with competition: an evolutionarily stable dynamic strategy

1993 ◽  
Vol 25 (2) ◽  
pp. 314-333 ◽  
Author(s):  
E. J. Collins ◽  
J. M. Mcnamara
Keyword(s):  
Job Search ◽  
Evolutionarily Stable Strategy ◽  
The Other ◽  
Control Limit ◽  
Search Problem ◽  
Limit Function ◽  
Reward Function ◽  
Infinite Population ◽  
Optimal Value ◽  
Evolutionarily Stable

We consider a game-theoretical solution for an optimal stopping problem which we describe in terms of a job-search problem with an infinite population of candidates and an infinite population of posts of varying value. Each candidate finds posts from the post population at unit rate. If a post found is still vacant, a candidate can either accept it or reject it. The reward to a candidate is the value of the post if one is accepted and zero if he never accepts a post. There are no costs for searching, no discounting of future rewards and no recall of previously found posts. The only pressure on a candidate to accept a post comes from the changing rate at which he finds vacant posts (and the values associated with them) as a result of the actions of the other candidates. It is not possible to define optimality for a single candidate without reference to the policies followed by the other candidates. We say a policy π is an evolutionarily stable strategy (ESS) if it has the following property: if all candidates used π and an individual candidate was given the option of changing his policy, then it would not be to his advantage to do so. We first find the optimal value function and optimal policy for the case of a single candidate operating in an environment where the distribution of posts on offer and the chance of finding one both vary with time in a known way. We then show that for the infinite population there is a unique ESS given by a control-limit policy π c, where the control-limit function c is the solution of a given differential equation with a given initial condition. This function c also gives the expected future reward function for any single candidate when all candidates use π c.

The job-search problem with competition: an evolutionarily stable dynamic strategy

1993 ◽  
Vol 25 (02) ◽  
pp. 314-333 ◽  
Author(s):  
E. J. Collins ◽  
J. M. Mcnamara
Keyword(s):  
Job Search ◽  
Evolutionarily Stable Strategy ◽  
The Other ◽  
Control Limit ◽  
Search Problem ◽  
Limit Function ◽  
Reward Function ◽  
Infinite Population ◽  
Optimal Value ◽  
Evolutionarily Stable

We consider a game-theoretical solution for an optimal stopping problem which we describe in terms of a job-search problem with an infinite population of candidates and an infinite population of posts of varying value. Each candidate finds posts from the post population at unit rate. If a post found is still vacant, a candidate can either accept it or reject it. The reward to a candidate is the value of the post if one is accepted and zero if he never accepts a post. There are no costs for searching, no discounting of future rewards and no recall of previously found posts. The only pressure on a candidate to accept a post comes from the changing rate at which he finds vacant posts (and the values associated with them) as a result of the actions of the other candidates. It is not possible to define optimality for a single candidate without reference to the policies followed by the other candidates. We say a policy π is an evolutionarily stable strategy (ESS) if it has the following property: if all candidates used π and an individual candidate was given the option of changing his policy, then it would not be to his advantage to do so. We first find the optimal value function and optimal policy for the case of a single candidate operating in an environment where the distribution of posts on offer and the chance of finding one both vary with time in a known way. We then show that for the infinite population there is a unique ESS given by a control-limit policy π c, where the control-limit function c is the solution of a given differential equation with a given initial condition. This function c also gives the expected future reward function for any single candidate when all candidates use π c.

A LARGE POPULATION, GAME THEORETIC MODEL OF JOB-SEARCH WITH DISCOUNTING

2009 ◽  
Vol 11 (03) ◽  
pp. 301-320
Author(s):  
DAVID M. RAMSEY
Keyword(s):  
Job Search ◽  
Evolutionarily Stable Strategy ◽  
Large Population ◽  
Iterative Algorithms ◽  
Theoretic Model ◽  
Search Problem ◽  
Game Theoretic ◽  
Population Game ◽  
Game Theoretic Model ◽  
Evolutionarily Stable

This paper considers a large population, game theoretic job-search problem, in which the ratio of job searchers to jobs is α. There are n distinct types of jobs, each with an associated value. Each searcher can only accept one job and cannot recall a job previously rejected. Once a searcher accepts a job, no other searcher can obtain that particular job. The reward to a searcher is taken to be the value of the job he/she obtains discounted by a factor of e-γt, where t is the time spent searching and γ > 0. It is shown that a unique evolutionarily stable strategy (ESS) exists for such problems. Two iterative algorithms for approximating the ESS are considered. In the case where there are only two types of job, the ESS can be calculated directly.

scholarly journals A Birandom Job Search Problem with Risk Tolerance

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Guoli Wang ◽  
Wansheng Tang ◽  
Ruiqing Zhao
Keyword(s):  
Job Search ◽  
Random Variables ◽  
Random Variable ◽  
Risk Tolerance ◽  
Wage Distribution ◽  
Search Process ◽  
Search Problem ◽  
Reservation Wage ◽  
Expected Return ◽  
Job Offers

This paper considers a novel class of birandom job search problem, in which the job offers are sampled by the job searcher from a finite job set with equivalent probability and their wages are characterized as independent but maybe not identically nonnegative random variables. The job searcher knows the job offer's wage distribution when he samples the job offer. Since the offered wage is a random variable and the reservation wage is a deterministic number, it is meaningless to make comparison directly. In order to rank the random wage and the reservation wage and provide decision support, a risk tolerance criterion is designed, and the job searcher then accepts or rejects the sampled job offer depending on whether the risk tolerance criterion is met or not. Since the offered wages are random variables and the search process is random, it's impossible to obtain the job searcher's real return; in this case, its expected value can be calculated via birandom theory. Simultaneously, some propositions on the expected return as well as the average search times are also studied which may provide some valuable suggestions to the job searcher. Numerical examples are given to illustrate the decision process of the risk tolerance-based birandom job search problem.

Employment, unemployment, and the job search in psychology.

1979 ◽  
Vol 34 (11) ◽  
pp. 1047-1060 ◽  
Author(s):  
Gary D. Gottfredson ◽  
Mary K. Swatko
Keyword(s):  
Job Search
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